We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and rehabilitative robotics with disabled-bodied subjects is the difficulty of collecting labeled training data. Muscle tone and spasticity often vary significantly among stroke subjects, and hand function can even change across different use sessions of the device for the same subject. We investigate the use of meta-learning to mitigate the burden of data collection needed to adapt high-capacity neural networks to a new session or subject. Our experiments on real clinical data collected from five stroke subjects show that MetaEMG can improve the intent inferral accuracy with a small session- or subject-specific dataset and very few fine-tuning epochs. To the best of our knowledge, we are the first to formulate intent inferral on stroke subjects as a meta-learning problem and demonstrate fast adaptation to a new session or subject for controlling a robotic hand orthosis with EMG signals.

翻译：我们提出MetaEMG，一种用于中风患者机器人手矫形器意图推断中快速适应的元学习方法。在针对残障人群的辅助与康复机器人机器学习中，一个关键挑战是标记训练数据的采集困难。中风患者的肌张力和痉挛程度常存在显著差异，且同一患者在不同设备使用周期中的手部功能也可能发生变化。我们研究利用元学习来减轻采集数据的工作负担，使高容量神经网络能够快速适应新的使用周期或患者。基于从五位中风患者采集的真实临床数据进行实验，结果表明，仅需少量周期或患者专属数据集以及极少的微调轮次，MetaEMG即可提升意图推断精度。据我们所知，我们首次将中风患者的意图推断问题形式化为元学习任务，并验证了通过肌电信号控制机器人手矫形器时对新周期或患者的快速适应能力。